sarthak247/codellama-7b-humaneval-java-fim

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codellama-7b-humaneval-java-fim

This model was trained from scratch on an this dataset for FIM task. It achieves the following results on the evaluation set:

• —Loss: 0.6155

Model description

Codellama-7b model trained for FIM on Java code dataset.

Intended uses & limitations

Bleh

Training and evaluation data

Dataset mentioned above

Training procedure

The following bitsandbytes quantization config was used during training:

• —quant_method: bitsandbytes
• —loadin8bit: False
• —loadin4bit: True
• —llmint8threshold: 6.0
• —llmint8skip_modules: None
• —llmint8enablefp32cpu_offload: False
• —llmint8hasfp16weight: False
• —bnb4bitquant_type: nf4
• —bnb4bitusedoublequant: True
• —bnb4bitcompute_dtype: bfloat16

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.0005
• —trainbatchsize: 4
• —evalbatchsize: 4
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 16
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_steps: 30
• —training_steps: 2000

Training results

Framework versions

• —PEFT 0.5.0
• —Transformers 4.34.0
• —Pytorch 2.1.0+cu118
• —Datasets 2.16.1
• —Tokenizers 0.14.1